Multi-Robot Informative Sampling and Coverage in GPS-Denied Environments
Aiman Munir, Ehsan Latif, Ramviyas Parasuraman
AI summary
Problem
Multi-robot systems struggle to localize themselves and perform effective informative path planning in GPS-denied environments, as existing methods either rely on unavailable global positioning or ignore how localization uncertainty degrades field estimation accuracy.
Approach
The authors propose AO-IPP, a framework that uses relative measurements from access points and dual Gaussian Process models to drive adaptive transitions between anchor localization, field sampling, and spatial coverage based on real-time uncertainty thresholds.
Key results
- Achieves mapping accuracy comparable to GPS-based algorithms
- Outperforms existing methods in balancing sampling and coverage by up to 54%
- Proves sublinear regret bounds for localization and coverage tasks
- Validated through extensive simulations and real-world multi-robot experiments
Why it matters
Provides a robust, scalable solution for autonomous environmental monitoring, exploration, and mapping in critical GPS-denied domains like indoor spaces, subterranean areas, and urban canyons.
Abstract
Multi-Robot Systems (MRS) in GPS-denied envi- ronments such as indoor spaces, subterranean areas, and urban canyons face the dual challenge of localizing themselves while performing informative path planning (IPP) to model unknown spatial fields. Current IPP methods rely heavily on GPS for localization, limiting their applicability in GPS-denied settings, while existing approaches addressing observation uncertainty fail to account for the localization uncertainty that degrades mapping accuracy. This paper presents Anchor-Oriented IPP (AO-IPP), a framework that coordinates robot teams through relative positioning using Access Points and uncertainty-driven transitions between three phases: anchor point localization, informative sampling for field estimation, and spatial coverage optimization. Each robot maintains dual Gaussian Process models with transitions driven by uncertainty levels rather than fixed time schedules. Extensive simulations and real-world experiments demonstrate that AO-IPP achieves performance comparable to GPS-based IPP algorithms while outperforming existing methods in balancing IPP and coverage objectives by up to 54%. The approach exhibits sublinear regret bounds and enables autonomous coordination in challenging environments previously inaccessible to traditional IPP methods, providing a robust solution for environmental monitoring, exploration, and mapping applications requiring both accurate field estimation and comprehensive spatial coverage.